Dynamic and scalable framework for flood early warning: Zambia case study
Flood forecasting and early warning systems (FFEWS) are vital for safeguarding vulnerable communities, enabling anticipatory action to mitigate livelihood and income losses before the disaster strikes. This study introduces a dynamic, and scalable framework for flood indicators in Zambia using Globa...
| Autores principales: | , , |
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| Formato: | Brief |
| Lenguaje: | Inglés |
| Publicado: |
2024
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| Acceso en línea: | https://hdl.handle.net/10568/170045 |
| _version_ | 1855533934206517248 |
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| author | Padhee, Suman Kumar Alahacoon, Niranga Amarnath, Giriraj |
| author_browse | Alahacoon, Niranga Amarnath, Giriraj Padhee, Suman Kumar |
| author_facet | Padhee, Suman Kumar Alahacoon, Niranga Amarnath, Giriraj |
| author_sort | Padhee, Suman Kumar |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Flood forecasting and early warning systems (FFEWS) are vital for safeguarding vulnerable communities, enabling anticipatory action to mitigate livelihood and income losses before the disaster strikes. This study introduces a dynamic, and scalable framework for flood indicators in Zambia using Global Precipitation Measurement GPM-IMERG (Integrated Multi-satellitE Retrievals for GPM) satellite rainfall data, Global Forecast SystemGFS global weather forecast data, and a GIS-based basin network database (BND). The framework is designed to enhance FFEWS through the CGIAR AWARE platform, strengthening anticipatory action mechanisms.
Evaluation of the GPM-IMERG data reveals a strong correlation with ground-based station observations and reliable performance metrics, confirming its suitability for real-time rainfall monitoring. To address limitations in ground data, the framework incorporates intensity duration frequency (IDF) curves across various return periods to generate flood early warning indicators. The BND ensures that flood warnings work for both individual sub-basins and their combined runoff responses.
A Comparison of three historical flooding events in Zambia as documented by EM-DAT, demonstrates the reliable performance of GPM-IMERG in identifying floods. A recent flooding event in the Southern province was analysed using GPM-IMERG and GFS data, showing reasonable forecasting of sub-basin level rainfall intensity and accumulation up to 10 days in advance, with accuracy improving closer to the event. This framework aims to bolster resilience and promote proactive disaster management through the AWARE platform, with the potential scalability across Africa and Asia. |
| format | Brief |
| id | CGSpace170045 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| record_format | dspace |
| spelling | CGSpace1700452025-12-08T10:11:39Z Dynamic and scalable framework for flood early warning: Zambia case study Padhee, Suman Kumar Alahacoon, Niranga Amarnath, Giriraj flood forecasting early warning systems frameworks communities indicators rainfall governance satellites weather forecasting resilience stakeholders case studies Flood forecasting and early warning systems (FFEWS) are vital for safeguarding vulnerable communities, enabling anticipatory action to mitigate livelihood and income losses before the disaster strikes. This study introduces a dynamic, and scalable framework for flood indicators in Zambia using Global Precipitation Measurement GPM-IMERG (Integrated Multi-satellitE Retrievals for GPM) satellite rainfall data, Global Forecast SystemGFS global weather forecast data, and a GIS-based basin network database (BND). The framework is designed to enhance FFEWS through the CGIAR AWARE platform, strengthening anticipatory action mechanisms. Evaluation of the GPM-IMERG data reveals a strong correlation with ground-based station observations and reliable performance metrics, confirming its suitability for real-time rainfall monitoring. To address limitations in ground data, the framework incorporates intensity duration frequency (IDF) curves across various return periods to generate flood early warning indicators. The BND ensures that flood warnings work for both individual sub-basins and their combined runoff responses. A Comparison of three historical flooding events in Zambia as documented by EM-DAT, demonstrates the reliable performance of GPM-IMERG in identifying floods. A recent flooding event in the Southern province was analysed using GPM-IMERG and GFS data, showing reasonable forecasting of sub-basin level rainfall intensity and accumulation up to 10 days in advance, with accuracy improving closer to the event. This framework aims to bolster resilience and promote proactive disaster management through the AWARE platform, with the potential scalability across Africa and Asia. 2024-01-25 2025-01-27T07:56:33Z 2025-01-27T07:56:33Z Brief https://hdl.handle.net/10568/170045 en Open Access application/pdf Padhee, Suman Kumar; Alahacoon, Niranga; Amarnath, Giriraj. 2024. Dynamic and scalable framework for flood early warning: Zambia case study. Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Initiative on Climate Resilience. 19p. |
| spellingShingle | flood forecasting early warning systems frameworks communities indicators rainfall governance satellites weather forecasting resilience stakeholders case studies Padhee, Suman Kumar Alahacoon, Niranga Amarnath, Giriraj Dynamic and scalable framework for flood early warning: Zambia case study |
| title | Dynamic and scalable framework for flood early warning: Zambia case study |
| title_full | Dynamic and scalable framework for flood early warning: Zambia case study |
| title_fullStr | Dynamic and scalable framework for flood early warning: Zambia case study |
| title_full_unstemmed | Dynamic and scalable framework for flood early warning: Zambia case study |
| title_short | Dynamic and scalable framework for flood early warning: Zambia case study |
| title_sort | dynamic and scalable framework for flood early warning zambia case study |
| topic | flood forecasting early warning systems frameworks communities indicators rainfall governance satellites weather forecasting resilience stakeholders case studies |
| url | https://hdl.handle.net/10568/170045 |
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